As artificial intelligence (AI) technologies become increasingly integrated into organizational operations, understanding the factors that drive AI-driven innovation performance is crucial. This study examines the roles of Digital Leadership (DL), Knowledge Management Effectiveness (KME) and AI Integration Capability (AIIC) in promoting Organizational Agility (OA) within Chinese industries. Using a mixed-methods approach and Structural Equation Modeling (SEM) with Smart Partial Least Squares (Smart PLS), the research analyzes data from 540 respondents across various sectors. The findings reveal significant direct effects of DL and AIIC on OA, as well as indirect effects mediated by DL and AIIC. Additionally, the study identifies moderation effects where AI-Driven Innovation Performance (AIDIP) enhances the relationships between KME and AIIC, as well as between AIIC and OA. The results highlight the importance of strategic leadership, effective knowledge management and robust AI integration capabilities in achieving organizational agility. The study contributes to the literature by providing a comprehensive model of AI-driven innovation performance and offers practical insights for organizations seeking to leverage AI technologies for competitive advantage. Limitations and future research directions are discussed, including the potential for global and longitudinal studies to further explore these relationships.

Android has become a dominant computing platform, and its popularity has coincided with a surge of malware. The incorporation of Security-Enhanced Linux in Android (SEAndroid) is an important security enhancement to the platform. While SEAndroid adds the benefits of mandatory protection that SELinux brought to desktops and servers, the protection is only as good as the policy. Existing Android devices contain a wide variety of SEAndroid policies, depending on both the version of Android as well as the device manufacturer. In this paper, we present an approach to analyze SEAndroid policies in conjunction with the underlying Linux/Unix Discretionary Access Control policies. We apply our approach to four different versions of Android Open Source Project (AOSP) as well as devices from seven different manufacturers, and find several forms of unintentional privilege assignments.

Firewall policies are notorious for having misconfiguration errors which can defeat its intended purpose of protecting hosts in the network from malicious users. We believe this is because today's firewall policies are mostly monolithic. Inspired by ideas from modular programming and code refactoring, in this work we introduce three kinds of modules: primary, auxiliary, and template, which facilitate the refactoring of a firewall policy into smaller, reusable, comprehensible, and more manageable components. We present algorithms for generating each of the three modules for a given legacy firewall policy. We also develop ModFP, an automated tool for converting legacy firewall policies represented in access control list to their modularized format. With the help of ModFP, when examining several real-world policies with sizes ranging from dozens to hundreds of rules, we were able to identify subtle errors.

Obligations are an important and indispensable part of many access control policies, such as those in DRM (Digital Rights Management) and healthcare information systems. To be able use obligations in a real-world access control system, there must exist a language for specifying obligations. However, such a language is currently lacking. XACML (eXtensible Access Control Markup Language), the current de facto standard for specifying access control policies, seems to integrate obligations as a part of it, but it treats obligations largely as black boxes, without specifying what an obligation should include and how to handle them. In this paper we examine the challenges in designing a practical approach for specifying and handling obligations, and then propose a language for specifying obligations, and an architecture for handling access control policies with these obligations, extending XACML's specification and architecture. In our design, obligations are modeled as state machines which communicate with the access control system and the outside world via events. We further implement our design into a prototype system named ExtXACML, based on SUN's XACML implementation. ExtXACML is extensible in that new obligation modules can be added into the system to handle various obligations for different applications, which shows the strong power of our design.

This paper focuses on the Participatory Sensing Network (PSN) that consists of selfish participants stimulated by certain reward programs. We propose a bargain-based mechanism to encourage cooperative message trading among the selfish nodes to maximize their rewards. We state the necessary condition for feasible message transactions in a theorem. We model message transaction as a two-person cooperative game, and we apply Nash Theorem to obtain optimal solution which is fair and Pareto optimal. We also present a greedy algorithm to reach the optimal solution. The effectiveness of the bargain-based stimulation mechanism is studied by extensive simulations based on real mobility traces.

Data transmission in self-organized multi-hop networks heavily depends on the cooperation among nodes. In many applications, however, the autonomous nodes exhibit selfish behaviors, aiming to optimize their own performance without consideration of other nodes in the network. Although a selfish node is interested in transmitting its own data only, part of its resource has to be traded for the cooperation of other nodes in the network, in order to establish a routing path through them to deliver data to its destination. In this paper, we propose a stimulating mechanism to encourage cooperation among the selfish nodes. Specifically, a credit-based Markov chain model is established to analyze the packet dropping probability, with given total bandwidth, bandwidth allocation, buffer space, and the maximum credit of each node. Based on the Markovian model, bandwidth allocation is optimized so that the dropping probability of a node’s own packets is minimum. It is a main contribution of this work to address the bandwidth constraint, which is a key resource in wireless networks but has been ignored in all existing incentive schemes. Extensive simulations are carried out to evaluate the proposed incentive scheme, and the simulation results show that it can effectively enable cooperation among selfish nodes and minimize overall packet dropping probability.

In this research, we study the self-maintenance scheduling problem in next generation wireless networks, with the consideration of resource maintenance constraints and resource conflicting constraints. We propose a linear programming (ILP) model and two heuristic algorithms, and evaluate their effectiveness and time complexity via analysis and simulations. Our results show that all of the proposed approaches can effectively schedule the requests within a reasonable period of time, but with different suitable scenarios. The ILP approach is effective when the number of requests is large and yields close-to-optimal results; the RC-Cliques-RM algorithm is suitable at the presence of many constraints; while the RC-RM-Cliques algorithm can scale to large size networks at the expense of reduced accuracy. It is anticipated that the proposed scheduling algorithms will be generally applicable to various mobile wireless networks where self-maintenance is needed.

In this paper, we propose a grid-based working node (WN) selection approach for wireless sensor networks. Due to coverage redundancy, it is highly desirable to identify a minimum subset of sensors in a wireless sensor network to serve as WNs, while the remaining sensors are deactivated to save power and reduce potential interference. The basic idea of our solution approach is to represent the coverage of the sensors by a number of sample points, i.e., the intersection points of the established grid. A simple approximation algorithm and a linear programming method are employed to select as few sensors as possible to cover all sample points. In order to reduce the computational time, clusters are formed and WN selection is performed within each cluster. The performance of the proposed WN selection schemes is quantified and the tradeoff among accuracy, communication overhead and computational time is evaluated via analyses and simulations.